What is a DisCoPy Functor?

A DisCoPy Functor is a mapping used to evaluate string diagrams by assigning concrete mathematical values (like matrices) to abstract boxes and types.

What categorical structures does the skill support?

It supports a wide hierarchy of structures including monoidal, braided, symmetric, traced, compact, pivotal, and ribbon categories.

Which tensor backends are supported by this skill?

The skill supports multiple backends including NumPy, PyTorch, and JAX for efficient tensor contraction and gradient calculation.

Can this skill be used for quantum computing research?

Yes, it includes specialized support for mapping diagrams to quantum circuits, state vectors, and density matrices via the discopy.quantum module.

How does it handle diagram optimization?

It provides methods for normalization and the application of interchange laws to simplify diagrams into their canonical normal forms before evaluation.

DisCoPy String Diagram Functors

Name: DisCoPy String Diagram Functors
Author: plurigrid

byplurigrid

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데이터 과학 및 ML

Evaluates monoidal functors for string diagrams to generate tensor networks, quantum circuits, and executable Python code.

The DisCoPy Functor skill enables Claude to manipulate and evaluate complex category-theoretic structures by mapping abstract string diagrams to concrete computational backends. By leveraging the DisCoPy library, this skill facilitates the transformation of morphisms into tensor networks (via NumPy, PyTorch, or JAX), quantum circuits, or functional Python logic. It is an essential tool for researchers and developers working in distributional compositional semantics (DisCoCat), categorical quantum mechanics, and diagrammatic reasoning, providing automated normalization, rewriting, and structure-preserving functorial mappings.

주요 기능

01Supports specialized functors for braided, symmetric, traced, and ribbon categories

02Translates abstract morphisms into executable quantum circuits and density matrices

03Automates diagram rewriting and normalization using operadic substitution and interchange laws

04Maps string diagrams to NumPy, PyTorch, or JAX tensor network backends

058 GitHub stars

06Integrates GF(3) conservation logic for verifying functorial compositions

사용 사례

01Simulating quantum algorithms and hardware-agnostic circuit generation

02Automating high-dimensional tensor contractions for scientific computing and machine learning

03Building Distributional Compositional (DisCo) models for Natural Language Processing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi discopy-functor

For use in Claude.ai and ChatGPT

Download Skill